NDLI: Analysis of information diffusion for threshold models on arbitrary networks

Content Provider	Springer Nature Link
Author	Lim, Sungsu Jung, Inwoo Lee, Seulki Jung, Kyomin
Copyright Year	2015
Abstract	Diffusion of information via networks has been extensively studied for decades. We study the general threshold model that embraces most of the existing models for information diffusion. In this paper, we first analyze diffusion processes under the linear threshold model, then generalize it into the general threshold model. We give a closed formula for estimating the final cascade size for those models and prove that the actual final cascade size is concentrated around the estimated value, for any network structure with node degrees ω(log n), where n is the number of nodes. Our analysis analytically explains the tipping point phenomenon that is commonly observed in information diffusion processes. Based on the formula, we devise an efficient algorithm for estimating the cascade size for general threshold models on any network with any given initial adopter set. Our algorithm can be employed as a subroutine for numerous algorithms for diffusion analysis such as influence maximization problem. Through experiments on real-world and synthetic networks, we confirm that the actual cascade size is very close to the value computed by our formula and by our algorithm, even when the degrees of the nodes are not so large.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	14346028
Journal	The European Physical Journal B
Volume Number	88
Issue Number	8
e-ISSN	14346036
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-08-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistical and Nonlinear Physics Condensed Matter Physics Physics Statistical Physics, Dynamical Systems and Complexity Fluid- and Aerodynamics Solid State Physics
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Condensed Matter Physics

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